Abstract: Quantum mechanics is a physical theory that offers a description for how physical systems (particularly very small ones) behave. It is at once curious, counter-intuitive, and remarkably accurate. In this talk, I will discuss the theory of quantum information, which provides an abstraction of the information-theoretic aspects of quantum mechanics. This abstraction forms the mathematical foundation of quantum information science, including quantum computation and quantum cryptography, which hold the potential for transformative changes in the way we process information. The theory of quantum information is also a beautiful and highly interdisciplinary subject that incorporates techniques from many areas of mathematics, such as functional analysis, matrix theory, abstract algebra, algebraic geometry, information theory, and computational complexity theory. In addition to describing some of its basic aspects, I will also discuss a few active areas of research and tantalizing open problems in quantum information theory.
2-3pm: Jude Kong (DIMACS/Princeton University):Modeling Microbial Dynamics: Effects on Environmental Health. Host: Junping Shi
Abstract: In this talk, I will present two nonlinear models for microbial dynamics vis-a-vis environmental health. Firstly, I will present a stoichiometric organic matter decomposition model in a chemostat culture that incorporates the dynamics of grazers. This mechanistic biodegradation model leads to reliable and suggestive ecological insights in the preservation and restoration of our fragile ecosystems. Using the model, I answer the following research questions: (i) What mechanisms allow microbes and resources to persist uniformly or go extinct? (ii) How do grazing and dead microbial residues affect decomposition? Secondly, I will talk about a stoichiometric mathematical model to predict methane emissions from oil sands tailings. Microbial metabolism of fugitive hydrocarbons produces greenhouse gas (GHG) emissions from oil sands tailings ponds and end pit lakes that retain semisolid wastes from surface mining of oil sands ores. Predicting GHG production, particularly methane, would help oil sands operators mitigate tailings emissions and would assist regulators evaluating the trajectory of reclamation scenarios.
3-4pm: Mikhail Chebotar (Kent State University): Around the Koethe Conjecture. Host: Chi-Kwong Li
Abstract: The Koethe Conjecture (whether a sum of two left nil ideals is nil) is one of the most famous open problems in Ring Theory and it inspired many interesting questions. We will discuss some recent progress and new directions for research in this area.
Abstract: The Riemann Hypothesis is an important unsolved problem in mathematics. It asserts that all the nontrivial zeros of the Riemann zeta-function have real part 1/2. The critical points of the Riemann zeta-function are also interesting, because they are intimately connected to the zeta zeros. In this talk, we will give an introduction to the Riemann zeta-function, discuss the distribution of the zeros and critical points, and show how they are related to some central problems in analytic number theory.
Abstract: In order to understand a complicated object in mathematics or in the real world, a straightforward but very useful approach is to separate the noise from the essence. This naturally leads to the following question: given a function f, is it possible to decompose it as f=f1+f2, where f1 is a function having an easy-to-explain structure, and f2 behaves like a random function?
The establishment of the Structure Theorems provided theoretically supports for this simple philosophy. Nowadays, Structure Theorems have been established in various areas in mathematics, including combinatorics, number theory and dynamical systems, each of which proves to have many important applications.
In this talk, I will introduce the development of the Structure Theorems in the areas mentioned above, with an emphasize on their applications in dynamical systems. This talk is based on joint works with Sebastian Donoso.
Abstract: Motivated by recent advances in technology for medical imaging and high-throughput genotyping, we consider an imaging genetics approach to discover relationships between the interplay of genetic variation and environmental factors and measurements from imaging phenotypes. We propose an image-on-scalar regression method, in which the spatial heterogeneity of gene-environment interactions on imaging responses is investigated via an ultra-high-dimensional spatially varying coefficient model (SVCM). Bivariate splines on triangulations are used to represent the coefficient functions over an irregular two-dimensional (2D) domain of interest. For the proposed SVCMs, we further develop a unified approach for simultaneous sparse learning (i.e., G×E interaction identification) and model structure identification (i.e., determination of spatially varying vs. constant coefficients). Our method can identify zero, nonzero constant and spatially varying components correctly and efficiently. The estimators of constant coefficients and varying coefficient functions are consistent and asymptotically normal. The performance of the method is evaluated by Monte Carlo simulation studies and a brain mapping study based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
Abstract: This talk will discuss two closely related problems, one in graph theory and one involving matrix rings. Given vertices $u$ and $v$ in a directed graph (digraph) $\Gamma$, we say that the ordered pair $(u, v)$ is a reachable pair if there exists a path of directed edges from $u$ to $v$. One may ask: if the digraph $\Gamma$ has $n$ vertices, then how many reachable pairs could $\Gamma$ contain? To answer this question, we translate it into an algebraic form and consider the problem of counting the number of nonzero entries in certain rings of $n \times n$ matrices. No prior knowledge will be assumed, and the talk should be accessible to undergraduates. This is joint work with Eric Swartz.
Abstract: An L-function is a type of generating function with multiplicative structure which arises from either an arithmetic-geometric object (like a number field, elliptic curve, abelian variety) or an automorphic form. The Riemann zeta function is the prototypical example of an L-function. While L-functions might appear to be an esoteric and special topic in number theory, time and again it has turned out that the crux of a problem lies in the theory of these functions. Many equidistribution problems in number theory rely on one's ability to accurately bound the size of L-functions; optimal bounds arise from the (unproven!) Riemann Hypothesis for the Riemann zeta function and its extensions to other L-functions. I will discuss some motivating equidistribution problems along with recent work (joint with K. Soundararajan) which produces new bounds for L-functions by proving a suitable "statistical approximation" to the (extended) Riemann Hypothesis.
Abstract: Start with four circles, all tangent to one another; then fill in the gaps between them with additional tangent circles. If you keep filling the gaps with smaller and smaller circles, you will generate an Apollonian circle packing. This picture has a rich history, from ancient Greece to Rene Descartes, to Japanese temple geometry. Amazingly, if you start with four circles whose curvatures are integers, then all the circles in the packing have this property. In my talk I'll describe some of the number theory that's been inspired by Apollonian packings in the last 20 years: theorems and conjectures about the growth of curvatures in a packing, and which integers can appear as curvatures. I will finish with my own work on the domain of multivariable power series defined by Apollonian packings.
Abstract: How many edges may a graph with no triangle have? Given a graph F, the Turan problem asks to maximize the number of edges in a graph on n vertices subject to the constraint that it does not contain F as a subgraph. In this talk, we will discuss constructions for this problem coming from finite geometry (eg using projective planes), combinatorial number theory, and "random polynomials".
We study an efficient numerical method for solving difficult `saddle point' linear systems that arise at every time step in the discretization of incompressible flow problems, including those modeled by the Navier-Stokes equations (e.g. water, oil, air under 220 mph) and magnetohydrodynamics (flows on conducting fluids). By combining an algebraic splitting of the block saddle point matrix, a particular approximation of the Schur complement system, and an incremental version of the associated time stepping algorithm, we are able to decompose the linear systems into smaller pieces that are easier to solve. We prove that the approximations made in the solve process are third (or fourth) order, and so are appropriate for use with second order time stepping methods. Numerical tests are performed which verify excellent performance of the methods on a variety of test problems.
Ramsey theory dates back to the 1930's and computing Ramsey numbers is a notoriously difficult problem in combinatorics. We study Ramsey numbers of graphs under Gallai colorings, where a Gallai coloring is a coloring of the edges of a complete graph such that no triangle has all its edges colored differently. Given a graph H and apositive integer k, the Gallai-Ramsey number of H is the least positive integer N such that every Gallai coloring of the complete graph K_N using k colors contains a monochromatic copy of H. Gallai-Ramsey numbers of graphs are more well-behaved, though computing them is far from trivial. In this talk, I will present our recent results on Gallai-Ramsey numbers of cycles.
Quantum information theory has emerged at the junction of multiple
disciplines, blending concepts and techniques from physics,
mathematics, and computer science. At the heart of this new field is
the effort to understand and answer the following questions: How is
information stored and manipulated in a quantum system, and how well
is this information preserved under physical processes? In this talk,
I will introduce some key notions in quantum information using
mathematical formalism, including quantum states, quantum channels,
and quantum entanglement. Some of the mathematical tools needed to
understand the problems of detecting and manipulating entanglement
will be presented.
In finite dimensional quantum information, transformations
between systems are represented by quantum channels: completely
positive and trace preserving linear maps between matrix spaces.
Single-shot quantum channel discrimination is the task of determining
which of two known channels is acting on a system, given only a single
use. We will review how entanglement can be used in this task to gain
an advantage, and how this phenomenon is directly connected to
properties of norms measuring the distance between the channels. In
particular, the advantage provided by entanglement is quantified by
the gap between the completely bounded trace norm and the induced
trace norm. We will discuss recent results related to these norms and
single-shot quantum channel discrimination, as well as open problems.
A beautiful example of spontaneous pattern formation appears in the distribution of vegetation in some dry-land environments.
Examples from Africa, Australia and the Americas reveal that vegetation, at a community scale, may spontaneously form into stripe-like bands, alternating with striking regularity with bands of bare soil, in response to aridity stress. A typical length scale for such patterns is
100 m; they are readily surveyed by modern satellites (and explored from your armchair in Google maps). These ecosystems represent some of Earth’s most vulnerable under threats of desertification, and some ecologists have suggested that the patterns, so easily monitored by satellites, may have potential as early warning signs of ecosystem collapse. I will describe efforts based in simple mathematical models, inspired by decades of physics research on pattern formation, to understand the morphology of the patterns. I will also describe efforts at analyzing the patterns via the satellite images, which, in some cases, we can accurately align with the aerial survey photographs from the 1950s to investigate details of the pattern evolution.
Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging (fMRI) data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.
In the Hilbert space formulation, quantum states are density matrices, i.e., positive
semidefinite matrices with trace one, and quantum channels are trace preserving completely positive linear maps on matrices. In this talk, we will present some results on the existence of quantum channels that send certain quantum states to other quantum states. Additional conditioons on the quantum channels may be imposed to satisfy certain
We consider bootstrap percolation in tilings of the plane by regular polygons. First, we determine the percolation threshold for each of the infinite Archimedean lattices.
More generally, let T denote the set of plane tilings t by regular polygons such that if t contains one instance of a vertex type, then t contains infinitely many instances of that type. We show that no tiling in T has threshold 4 or more.
This material is self-contained, and requires no particular background. We'll share many open problems, as well as the intuition behind these results.
This will be an introduction to how the graph (of a real symmetric matrix, or a general matrix) constrains the multiplicities of its eigenvalues. The case of trees is most interesting and this will be described in some detail, including the maximum multiplicity, the minimum number of distinct eigenvalues, the possible lists of multiplicities and how they come about. This talk will be an overview of the subject and a second talk in the GAG seminar will continue the description. The subject has just been covered in a new book from Cambridge University Press (same title), and REU students have helped to make some very important contributions over the years. There is still plenty of work to be done in the area, which has been of interest to algebraic graph theorists and numerical analysts, as well as matrix theorists.